BCR-Net: Boundary-Category Refinement Network for Weakly Semi-Supervised X-Ray Prohibited Item Detection with Points

TL;DR

BCR-Net is a novel boundary-category refinement network designed for weakly semi-supervised detection of prohibited items in X-ray images, effectively balancing annotation costs and detection accuracy.

Contribution

It introduces boundary and category refinement modules with attention and contrastive learning to improve detection with limited annotations.

Findings

Achieves significant performance improvements over state-of-the-art methods.

Effectively handles imprecise localization and classification issues.

Operates well with limited box annotations and abundant point annotations.

Abstract

Automatic prohibited item detection in X-ray images is crucial for public safety. However, most existing detection methods either rely on expensive box annotations to achieve high performance or use weak annotations but suffer from limited accuracy. To balance annotation cost and detection performance, we study Weakly Semi-Supervised X-ray Prohibited Item Detection with Points (WSSPID-P) and propose a novel \textbf{B}oundary-\textbf{C}ategory \textbf{R}efinement \textbf{Net}work (\textbf{BCR-Net}) that requires only a few box annotations and a large number of point annotations. BCR-Net is built based on Group R-CNN and introduces a new Boundary Refinement (BR) module and a new Category Refinement (CR) module. The BR module develops a dual attention mechanism to focus on both the boundaries and salient features of prohibited items. Meanwhile, the CR module incorporates contrastive branches into the heads of RPN and ROI by introducing a scale- and rotation-aware contrastive loss, enhancing intra-class consistency and inter-class separability in the feature space. Based on the above designs, BCR-Net effectively addresses the closely related problems of imprecise localization and inaccurate classification. Experimental results on public X-ray datasets show the effectiveness of BCR-Net, achieving significant performance improvements to state-of-the-art methods under limited annotations.